Research Line
QSAR & predictive modelling
Interpretable machine-learning models for ADMET, target activity and toxicity prediction. We build, benchmark and deploy them with our SIBILA AutoML framework, and expose them as target-specific web servers for diabetes, obesity, anti-aging and natural compounds.
What we work on
Concrete predictive modelling problems we solve for in-house programs and external partners.
- ADMET prediction — absorption, distribution, metabolism, excretion and toxicity models for hit triage.
- Target activity prediction — QSAR models trained on curated datasets, with applicability domain analysis.
- Toxicity and safety — hERG, cytotoxicity and tissue-specific risk models.
- Interpretability — every prediction is delivered with feature attribution (SHAP, LIME, descriptor importance), not as a black box.
- Target-specific servers — public web tools for anti-diabetic, anti-obesity, anti-aging and antioxidant activity.
Tools we use
- SIBILA — AutoML platform for interpretable predictive models.
- DIA-DB — diabetes drug prediction by similarity and inverse virtual screening.
- OBE-DB — anti-obesity drug prediction.
- AntiAge-DB — natural cosmetic anti-aging compound prediction.
Applications & target areas
Where interpretable QSAR is delivering value for our partners today.
Pharma R&DEarly ADMET and toxicity filtering of compound libraries before in vitro validation.
Metabolic diseaseAnti-diabetic and anti-obesity activity prediction for repurposing and natural products.
CosmeticsAnti-aging ingredient screening through AntiAge-DB and custom QSAR models.
Clinical & environmentalCardiovascular risk, hospital-readmission and drought-monitoring models built on the same SIBILA stack.
Selected resources
- SIBILA — interpretable AutoML platform — DOI 10.3390/ai5040116
- DIA-DB — diabetes drug prediction server — DOI 10.1021/acs.jcim.0c00107
- OBE-DB — anti-obesity drug prediction — preprint 10.1101/2025.04.10.648110
- AntiAge-DB — cosmetic anti-aging natural compound predictions — DOI 10.3390/antiox11112268
Interested in this line?
Contact Prof. Horacio Pérez-Sánchez · hperez@ucam.edu